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| "title": "We are grateful to our invited speakers for their engaging presentations and insights they brought to the workshop. We would further like to thank the workshop chairs, Gao Wei and Lu Wang, for their guidance and support in organising the workshop, as well as the remote presentation chair, Zhongqing Wang, for the hard work in preparing the workshop page. Finally, we are very grateful to all the authors who submitted and presented their work to LoResMT. On behalf of the organizing committee", | |
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| { | |
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| "abstract": [], | |
| "body_text": [ | |
| { | |
| "text": "The need for receiving relevant, fast and up-to-date information in one's language is today more important than ever, especially under the current crisis conditions. Machine Translation (MT) is a vital tool for facilitating communication and access to information. For most of the world's languages, the lack of training data has long posed a major obstacle to developing high quality MT systems, excluding the speakers of these low resource languages from the benefits of MT. In the past few years, MT performance has improved significantly, mainly due to the new possibilities opened up by Neural Machine Translation (NMT). With the development of novel techniques, such as multilingual translation and transfer learning, the use of MT is no longer a privilege restricted to users of a dozen popular languages. Consequently, there has been an increasing interest in the MT community to expand the coverage to more languages with different geographical presence, degree of diffusion and digitalization. Today, research groups are working on MT in all continents. The number of languages offered by publicly available MT engines is increasing, reaching almost 200 languages at the moment of writing. We are witnessing an interesting phenomenon of collaborative projects to promote MT for under-represented languages, involving partners from all over the globe, participating on a voluntary basis. These developments have created a colourful, promising future for low resource languages on the machine translation map.", | |
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| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Preface", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Despite all these encouraging developments in MT technologies, creating an MT system for a new language from scratch or even improving an existing system still requires a considerable amount of work in collecting the pieces necessary for building such systems. Due to the data-hungry nature of NMT approaches, the need for parallel and monolingual corpora in different domains is never saturated. The development of MT systems requires reliable test sets and evaluation benchmarks. In addition, MT systems still rely on several NLP tools to pre-process human-generated texts in the forms that are required as input for MT systems and post-process the MT output in proper textual forms in the target language. These NLP tools include, but are not limited to, word tokenizers/de-tokenizers, word segmenters, morphological analysers. The performance of these tools has a great impact on the quality of the resulting translation. There is only limited discussion on these NLP tools, their methods, their role in training different MT systems, and their coverage of support in the many languages of the world.", | |
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| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Preface", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "LoResMT provides a discussion panel for researchers working on MT systems/methods for low resource and under-represented languages in general. This year we received research papers covering a wide range of languages spoken in Asia, Latin America, Africa and Europe. These languages are: Ashaninka, Assamese, Bambara, Bengali, Bhojpuri, Bicolano, Cebuano, English, Esperanto, French, Greek, Hiligaynon, Hindi, Ilocano, Kurdish, Manipuri, Pangasinense, Russian, Tagalog, Tamil, Vietnamese. We received both resource papers (monolingual, parallel corpora, formalisms) and methods papers, ranging from unsupervised, zero-shot to multilingual NMT. The acceptance rate of LoResMT this year is 60%.", | |
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| "ref_spans": [], | |
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| "section": "Preface", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "In addition to the research papers, the workshop organised a shared task, where we solicit participants to submit novel zero-shot NMT systems for the language pairs Hindi-Bhojpuri, Hindi-Magahi and Russian-Hindi. Two shared task papers are archived in the proceedings, along with the findings of the shared task.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Preface", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "LoResMT hosts two invited talks. The first one is given by Grace Tang and Alp \u00d6ktem from Translators without Borders, who presented Translators without Borders' (TWB) Gamayun project where they aim to enable two-way communication using language technology for marginalized language speakers. In the second invited talk, Bonaventure Dossou and Chris Emezue from Jacobs University Bremen & Technical University of Munich describe the challenges of creating an NMT system for a new language pair, Fon-French.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Preface", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "We would sincerely like to thank all of our program committee members for their valuable help in reviewing the submissions and for providing their constructive feedback for improving the workshop: Alberto Poncelas, Amirhossein Tebbifakhr, Anna Currey, Arturo Oncevay, Atul Kr. Ojha ", | |
| "cite_spans": [ | |
| { | |
| "start": 277, | |
| "end": 281, | |
| "text": "Ojha", | |
| "ref_id": null | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
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| "sec_num": null | |
| } | |
| ], | |
| "back_matter": [ | |
| { | |
| "text": "Friday, December 04, 2020 (Times GMT+8) 08:00-08:15 Opening remarks 09:00-11:00 Q&A Session 1 (Asia, Americas) ", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 26, | |
| "end": 39, | |
| "text": "(Times GMT+8)", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Conference Program", | |
| "sec_num": null | |
| } | |
| ], | |
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| "raw_text": "Towards Machine Translation for the Kurdish Language Sina Ahmadi and Maraim Masoud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87", | |
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| "BIBREF12": { | |
| "ref_id": "b12", | |
| "title": "An Ensemble Method for Producing Word Representations focusing on the Greek Language Michalis Lioudakis, Stamatis Outsios and Michalis Vazirgiannis", | |
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| "BIBREF13": { | |
| "ref_id": "b13", | |
| "title": "Investigating Low-resource Machine Translation for English-to-Tamil Akshai Ramesh, Venkatesh Balavadhani parthasa, Rejwanul Haque and", | |
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| "raw_text": "Investigating Low-resource Machine Translation for English-to-Tamil Akshai Ramesh, Venkatesh Balavadhani parthasa, Rejwanul Haque and Andy Way . . . . . . . . . . . 118 vii", | |
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| "TABREF0": { | |
| "text": ", Bharathi iii Shantipriya Parida, Idiap Research Institute Surafel Melaku Lakew, Fondazione Bruno Kessler Tommi A Pirinen, University of Hamburg Valentin Malykh, Huawei Noah's Ark lab and Kazan Federal University", | |
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| "content": "<table><tr><td>Additional Reviewers:</td></tr><tr><td>Sunit Bhattacharya, Charles University, Prague</td></tr><tr><td>Invited Speakers</td></tr><tr><td>Grace Tang, Translators Without Borders</td></tr><tr><td>Alp \u00d6ktem, Translators Without Borders</td></tr><tr><td>Bonaventure Dossou, Jacobs University Bremen</td></tr><tr><td>Chris Emezue, Technical University of Munich</td></tr><tr><td>vi</td></tr></table>", | |
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